Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study.

OBJECTIVE: Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. METHODS: Twelve emergency departments provided 3 years of retrospective administrative data from Australia (2017-2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020). RESULTS: There were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period. CONCLUSIONS: Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.

Predicting Ambulance Patient Wait Times: A Multicenter Derivation and Validation Study.

STUDY OBJECTIVE: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door-to-off-stretcher wait times that are applicable to a wide variety of emergency departments. METHODS: Nine emergency departments provided 3 years (2017 to 2019) of retrospective administrative data from Australia. Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine-learning models were developed to predict wait times at each site and were internally and externally validated. RESULTS: There were 421,894 episodes analyzed, and median site off-load times varied from 13 (interquartile range [IQR], 9 to 20) to 29 (IQR, 16 to 48) minutes. The global site prediction model median absolute errors were 11.7 minutes (95% confidence interval [CI], 11.7 to 11.8) using linear regression and 12.8 minutes (95% CI, 12.7 to 12.9) using elastic net. The individual site model prediction median absolute errors varied from the most accurate at 6.3 minutes (95% CI, 6.2 to 6.4) to the least accurate at 16.1 minutes (95% CI, 15.8 to 16.3). The model technique performance was the same for linear regression, random forests, elastic net, and rolling average. The important variables were the last k-patient average waits, triage category, and patient age. The global model performed at the lower end of the accuracy range compared with models for the individual sites but was within tolerable limits. CONCLUSION: Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables.

Displaying emergency patient estimated wait times: A multi-centre, qualitative study of patient, community, paramedic and health administrator perspectives.

OBJECTIVE: EDs have the potential ability to predict patient wait times and to display this to patients and other stakeholders. Little is known about whether consumers and stakeholders would want this information and how wait time predictions might be used. The aim of the present study was to gain perspectives from consumers and health services personnel regarding the concept of emergency wait time visibility. METHODS: We conducted a qualitative interview and focus group study in 2019. Participants included emergency medicine patients, families, paramedics, well community members, and hospital/paramedic administrators from multiple EDs and organisations in Victoria, Australia. Transcripts were coded and themes presented. RESULTS: One focus group and 103 semi-structured interviews were conducted in 2019 including 32 patients, 22 carers/advocates and 21 paramedics in the ED; 20 health service administrators (paramedic and hospital) and 15 community members. Consumers and paramedics face physical and psychological difficulties when wait times are not visible. Consumers believe about a 2-h wait is tolerable, beyond this most begin to consider alternative strategies for seeking care. Consumers want to see triage to doctor times; paramedics want door-to-off stretcher times (for all possible transport destinations); with 47 of 50 consumers and 30 of 31 paramedics potentially using this information. About 28 of 50 consumers would use times to inform facility or provider choice, another 19 of 50 want information once in the waiting room. During prolonged waits, 51 of 52 consumers would continue to seek care. CONCLUSIONS: Consumers and paramedics want wait time information visibility. They would use the information in a variety of ways, both pre-hospital and while waiting for care.